Solar Physics

, 294:160 | Cite as

Neural Network for Solar Irradiance Modeling (NN-SIM)

  • Steffen MauceriEmail author
  • Odele Coddington
  • Danielle Lyles
  • Peter Pilewskie
Part of the following topical collections:
  1. Irradiance Variations of the Sun and Sun-like Stars


An understanding of solar variability over a broad range of wavelengths and timescales is needed by scientists studying Earth’s climate. While the current understanding of solar irradiance from measurements and models is maturing, there remain notable areas of discrepancy that highlight a lack of understanding of the variability in solar spectral irradiance (SSI) on 27-day solar-rotational timescales and longer, and in total solar irradiance (TSI) at solar-cycle timescales and longer. The sources of instrumental noise and instability suspected behind differences in independent measurement records are actively debated. Furthermore, estimates from solar-irradiance empirical-proxy models and semi-empirical models also differ from each other and from the observations by varying degrees. To investigate whether models and observations can be brought into closer agreement we developed a novel, data-driven, solar-irradiance model using an ensemble of feed-forward artificial neural networks. Key features of our model architecture include a non-linear relationship between solar-activity proxy and irradiance with a high degree of freedom that comes from the incorporation of a greater number of solar-activity proxies than previous proxy models. Furthermore, we utilize a recent re-analysis of solar spectral irradiance (SSI) observations, stemming from a new degradation-correction methodology, to develop our model. Our approach, the Neural Network for Solar Irradiance Modeling (NN-SIM), reconstructs total solar irradiance and SSI from 205 nm to 2300 nm and from 1979 to the present day. We find close agreement between NN-SIM and various observational records as well as independent models. NN-SIM is available at


Solar irradiance Solar irradiance model Neural network 



We thank the scientists and engineers that made the many solar-irradiance and solar-proxy datasets available that we used to train and validate our model. For comparison, we made use of the SATIRE-S and EMPIRE solar irradiance models. Furthermore, we compared to SORCE/SIM SSI, SORCE/SOLSTICE SSI and Mg ii index, Aura/OMI SSI, SORCE/TIM TSI and the TSIc, PMOD, ACRIM, RMIB TSI composites. Finally, the Mg ii index from the University of Bremen, Ly \(\alpha \) composite from, \(\mbox{F}_{10.7}, \mbox{F}_{15}, \mbox{F}_{30}\) solar flux from, and SORCE/TIM were directly used for NN-SIM. We acknowledge receipt from PMOD/WRC, Davos, Switzerland, of the updated dataset 42_65_1709 with new data from the VIRGO experiment on the cooperative ESA/NASA Mission SOHO. Additionally, we acknowledge that the the development of the independently derived uncertainties for PSI, Ly \(\alpha\), and TSI received funding from the European Community’s Seventh Framework Programme (FP7 2012) Project SOLID ( under grant agreement no 313188. Furthermore, we acknowledge the collaboration with the International Team on “Scenarios of Future Solar Activity for Climate Modelling” at the International Space Science Institute (ISSI, Bern), and the COST Action TOSCA (Towards a more complete understanding of the solar influence on climate).

This research was supported in part by 80NSSC18K1304 and NNX15AK59G.

Disclosure of Potential Conflicts of Interest

The authors declare that they have no conflicts of interest.


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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Atmospheric and Oceanic SciencesCU BoulderBoulderUSA
  2. 2.Laboratory for Atmospheric and Space PhysicsCU BoulderBoulderUSA
  3. 3.Department of Applied MathematicsCU BoulderBoulderUSA

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